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Obj 


data = None SIMPLE REGRESSION / CLASSIFICATION  

train = None


valid = None


test = None


n_classes = None WHEN INPUTS ARE FIXEDSIZE GREYSCALE IMAGES  

img_shape = None When inputs 'x' must somehow be preprocessed, processor is a function that will take care of it. 

preprocess = None TIMESERIES  

dataSIMPLE REGRESSION / CLASSIFICATION  In this setting, you are aiming to do vector classification or vector regression where your train, valid and test sets fit in memory. The convention is to put your data into numpy ndarray instances. Put training data in the `train` attribute, validation data in the `valid` attribute and test data in the `test attribute`. Each of those attributes should be an instance that defines at least two attributes: `x` for the input matrix and `y` for the target matrix. The `x` ndarray should be one example per leading index (row for matrices). The `y` ndarray should be one target per leading index (entry for vectors, row for matrices). If `y` is a classification target, than it should be a vector with numpy dtype 'int32'. If there are weights associated with different examples, then create a 'weights' attribute whose value is a vector with one floatingpoint value (typically doubleprecision) per example. If the task is classification, then the classes should be mapped to the integers 0,1,...,N1. The number of classes (here, N) should be stored in the `n_classes` attribute.

n_classesWHEN INPUTS ARE FIXEDSIZE GREYSCALE IMAGES  In this setting we typically encode images as vectors, by enumerating the pixel values in lefttoright, toptobottom order. Pixel values should be in floatingpoint, and normalized between 0 and 1. The shape of the images should be recorded in the `img_shape` attribute as a tuple (rows, cols).

img_shapeWhen inputs 'x' must somehow be preprocessed, processor is a function that will take care of it. A cleaner (transparent) alternative would be for x to wrap the data intelligently.

preprocessTIMESERIES  When dealing with examples which are themselves timeseries, put each example timeseries in a tensor and make a list of them. Generally use tensors, and resort to lists or arrays wherever different

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